博文
DIKWP Model Based Changes in Academic Evaluation
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Traditional Invention and Innovation Theory 1946-TRIZ Does Not Adapt to the Digital Era
-Innovative problem-solving methods combining DIKWP model and classic TRIZ
Purpose driven Integration of data, information, knowledge, and wisdom Invention and creation methods: DIKWP-TRIZ
(Chinese people's own original invention and creation methods:DIKWP - TRIZ)
DIKWP Model Based Changes in Academic Evaluation
Yucong Duan, 
Shiming Gong
DIKWP-AC Artificial Consciousness Laboratory
AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory
World Association of Artificial Consciousness
(Email：duanyucong@hotmail.com)
Catalogue
1 Profound Changes in Traditional Peer Review Mechanisms Disrupted by AI Technology
3 Changes and impacts on the current academic evaluation mechanism
3.2 Change of influencing factors
3.3 The new evaluation mechanism of highly cited papers
3.4 The overall impact on the academic evaluation system
4 Based on DIKWP model impact analysis
6 Realization of the reform of traditional academic evaluation mechanism
6.1 Initial stage: building the basic framework
6.2 Development Stage: Integration and Optimization
6.3 Mature stage: intelligent decision-making and social feedback
6.4 Long-term Maintenance and Innovation
7.1 Convergence stage: integration of data and information
7.2 Integration stage: deepening of knowledge and wisdom
7.3 Transition stage: introduction of purpose level and dynamic adjustment of evaluation system
7.4 Improvement stage: building a comprehensive evaluation platform based on DIKWP
7.5 Implementation stage: continuous optimization and community participation
With the rapid development of artificial intelligence technology, academic evaluation mechanism is facing unprecedented challenges and opportunities. Traditional evaluation mechanisms, such as SCI retrieval, impact factors and highly cited papers, are widely used and recognized in academic circles, but there are limitations in the comprehensiveness, dynamics and participation of evaluation. This report discusses a new academic evaluation mechanism based on DIKWP model (data, information, knowledge, wisdom and purpose). This mechanism is committed to connecting and integrating traditional and modern evaluation methods. By integrating artificial intelligence technology and DIKWP model, a more comprehensive, fair and transparent academic evaluation system is proposed. The report describes in detail the design principles, implementation steps and expected effects of the new evaluation mechanism, and how to ensure the adaptability and effectiveness of the evaluation system through community participation and continuous optimization. Through this reform, it aims to promote the healthy development of academic research, encourage innovative research, and provide researchers with a more diverse and participatory evaluation environment.
1 Profound Changes in Traditional Peer Review Mechanisms Disrupted by AI Technology
In the scenario of the traditional peer review mechanism under the subversion of AI technology since 2023, we can foresee a series of profound changes:
Automated preliminary screening process: AI technology can automatically analyze submitted academic papers and conduct preliminary screening based on preset quality standards and relevance standards. This not only greatly reduces the burden of manual screening, but also improves the efficiency and fairness of the screening process.
In-depth content analysis: AI system can deeply analyze the contents of papers and identify key factors such as novelty, accuracy of research methods and effectiveness of data analysis. Through deep learning and natural language processing technology, AI can understand complex academic papers and provide review opinions comparable to or even beyond those of human experts.
Predicting the influence of papers: Using big data and machine learning algorithms, AI technology can predict the citation and academic influence of papers in the future. This provides an important reference for editors and reviewers, and helps them identify those research works that may have a significant impact on the academic community.
Detection of academic misconduct: AI technology can effectively detect academic misconduct such as plagiarism, data forgery and repeated publication in papers. Through the analysis of massive literature database, AI can quickly identify the possible problems in the paper and ensure the academic purity.
Personalized matching experts: AI system can automatically match the most suitable evaluation experts according to the content and research direction of the paper. This process takes into account experts' research interests, professional background and review history, and ensures that each paper can get the most fair and professional review.
Real-time feedback and interaction system: AI technology can establish a platform to allow more direct and real-time communication between authors and reviewers. This interaction not only speeds up the evaluation process, but also improves the transparency and interactivity of the evaluation.
Continuous learning and self-optimization: With the passage of time, the quality and accuracy of AI system evaluation will continue to improve by continuously learning new data and evaluation results. This ability of self-optimization makes AI technology an evolving system, which can adapt to the ever-changing needs of academic circles.
The traditional peer review mechanism under the subversion of AI technology will become more efficient, fair and intelligent. This reform not only improves the quality and speed of academic publishing, but also provides researchers with a more convenient and interactive academic exchange environment, which promotes the progress and innovation of the entire academic community.
In the next few years, we can expect that the application of AI technology in peer review mechanism will go through the process from initial attempt to full implementation. The following is the possible time course of these changes:
2024-2025: Preliminary attempt and verification
Research stage: Academia and technology developers began to cooperate to study how to effectively apply AI technology to peer review. During this period, a small number of pilot projects will be launched to verify the effectiveness of AI in automatic preliminary screening, content analysis and detection of academic misconduct.
Technical verification: The successful cases in this stage will prove the potential of AI technology in improving the efficiency and accuracy of evaluation, but at the same time, it will also expose the initial challenges of technology implementation, such as the formulation of evaluation standards and the quality of training data of AI system.
2026-2027: technical perfection and initial application
Technical iteration: According to the feedback from the initial test, technical developers will constantly improve the AI system, improve the algorithm and expand the database to adapt to a wider range of academic fields and complex review needs.
Preliminary application: More academic journals and conferences began to use AI technology for preliminary screening and content analysis of papers, and AI-assisted peer review became the new normal accepted by more and more editors and scholars.
2028-2029: widespread application and community feedback
Widely used: With the maturity of AI technology and the improvement of community trust in AI-assisted review mechanism, more and more academic publications and organizations begin to fully adopt AI technology for peer review.
Community feedback: The academic community began to conduct extensive discussions and feedback on the effectiveness of the AI review mechanism, further promoting the optimization and improvement of technology. At the same time, ethical and moral discussions on AI review will emerge to promote the transparency and fairness of the review mechanism.
2030 and beyond: continuous optimization and deep integration
Continuous optimization: Based on the continuously collected data and feedback, the AI system will enter the stage of continuous optimization and learning, and its evaluation quality and accuracy will gradually surpass or reach the level of human experts.
Deep integration: AI technology will not only play a role in peer review, but also deeply integrate into all aspects of academic research, including research design, data analysis, result verification, etc., forming a comprehensive and intelligent academic research and publishing ecology.
The application of AI technology in peer review mechanism will be a gradual development process, from the initial attempt and verification to the perfection and wide application of technology, and then to the deep integration and continuous optimization. This process requires not only technological progress, but also the joint efforts and adaptation of academia, technology developers and all walks of life. With the passage of time, we can foresee the arrival of a new era of more efficient, fair and intelligent peer review.
3 Changes and impacts on the current academic evaluation mechanism
With the wide application of artificial intelligence technology in peer review, we will witness profound changes in the current academic evaluation mechanism, especially the impact on important indicators such as SCI retrieval, impact factors and highly cited papers. The following are the specific impacts and changes that these changes may bring:
Wider inclusiveness: AI technology can identify and evaluate the value of interdisciplinary research more efficiently, which may lead to more fields of research in SCI retrieval system and increase the visibility of interdisciplinary research.
Retrieval quality improvement: AI-assisted retrieval system can understand the research content and context more accurately, improve the relevance and accuracy of retrieval results, and thus improve the efficiency of researchers' retrieval of documents.
3.2 Change of influencing factors
Diversification of evaluation criteria: AI technology will make the calculation of impact factors not only depend on the number of citations, but also include multi-dimensional evaluation criteria such as social impact and interdisciplinary impact of papers.
Real-time dynamic update: With the real-time data processing ability of AI, the impact factor may become a dynamically updated indicator, which more truly reflects the real-time influence of journals or articles.
3.3 The new evaluation mechanism of highly cited papers
Emphasis on content quality: the evaluation of highly cited papers may no longer depend only on the number of citations. AI technology can deeply analyze the innovation, academic quality and social value of papers and form a more comprehensive evaluation mechanism.
Rapid discovery and popularization: AI technology can quickly identify potential high-quality research, promote its rapid spread and application in academic circles, and accelerate the iterative update of scientific knowledge.
3.4 The overall impact on the academic evaluation system
Improvement of fairness and transparency: The application of AI technology will reduce the influence of artificial prejudice and subjectivity, and improve the fairness and transparency of academic evaluation.
Enhanced adaptability and flexibility: With the continuous development and changes of academic research, AI technology can quickly adapt to new academic trends and evaluation needs, making the academic evaluation mechanism more flexible and effective.
Promote academic innovation: AI-assisted evaluation mechanism will pay more attention to the originality and innovation of research and encourage scholars to explore new fields and methods, thus promoting academic innovation and scientific progress.
Artificial intelligence technology will have a far-reaching impact on the existing academic evaluation mechanism and promote its development in a more fair, transparent, pluralistic and dynamic direction. These changes will help to better motivate and evaluate academic achievements and promote the healthy development of academic research.
4 Based on DIKWP model impact analysis
With the help of DIKWP model, we can understand the influence of artificial intelligence technology on the reform of academic evaluation mechanism from the perspective of data, information, knowledge, wisdom and purpose (DIKWP):
Data
In the process of reform, data, as the basic level, refers to the original information about journal citation, paper quality, interdisciplinary research and so on. Artificial intelligence technology can effectively process and analyze these large and complex data, and provide more accurate and comprehensive basic support for academic evaluation. For example, through the deep learning algorithm, key data can be extracted from the literature, such as citation times, research fields, author contributions, etc., which lays the foundation for subsequent information extraction and knowledge formation.
Information
The information level involves transforming raw data into useful information, such as identifying high-quality research and potential interdisciplinary influence through algorithm analysis. The application of AI technology at this level makes the information extracted from a large number of data more accurate and targeted, and provides more abundant and in-depth content for academic evaluation.
Knowledge
The knowledge level focuses on extracting and constructing valuable knowledge from information, such as understanding the deep meaning of influencing factors and constructing evaluation standards for interdisciplinary research. Through in-depth analysis and learning of information, AI technology helps to form an understanding and knowledge of new academic evaluation standards, thus promoting the innovation and optimization of evaluation mechanism.
Wisdom
On the intellectual level, it emphasizes the application of knowledge to make wise decisions and judgments, such as deciding which new evaluation criteria should be introduced into the academic evaluation system. The application of AI technology at this level can provide strategic suggestions for the improvement of academic evaluation mechanism based on in-depth knowledge and data analysis, and ensure the fairness, comprehensiveness and foresight of evaluation criteria.
Purpose
Finally, the purpose level focuses on the long-term goals achieved by reforming the academic evaluation mechanism, such as promoting academic innovation, ensuring the quality of research, and encouraging academic exchanges. Through the application of DIKWP model, the introduction of artificial intelligence technology is not only a means to change the evaluation mechanism, but also to achieve a higher level goal-to create a healthier, fairer and more innovative academic ecosystem.
DIKWP model provides us with a comprehensive analysis of how artificial intelligence technology affects the reform of academic evaluation mechanism. From data collection and processing, to information extraction, to knowledge construction and wisdom application, until the ultimate purpose of evaluation mechanism reform is realized, artificial intelligence technology will play a key role at every level, and promote the development of academic evaluation in a more scientific, reasonable and innovative direction.
5 DIKWP academic evaluation mechanism in the context of the transformation from conceptual space to semantic space
Based on the DIKWP model and the transformation from conceptual space to semantic space, a new academic evaluation mechanism is envisaged, which not only pays attention to traditional quantitative indicators (such as citation times and impact factors), but also digs into the semantic value and social impact of academic achievements, so as to evaluate the quality and contribution of research more comprehensively. The following is the imagination of the new evaluation mechanism designed based on DIKWP model:
Data level
Automatic data collection: use AI technology to automatically collect raw data about academic publications, including but not limited to citation times, downloads, social media mentions, etc.
Multidimensional data integration: In addition to traditional indicators, non-traditional data indicators such as interdisciplinary research, diversity of research teams and openness of research (such as open access to publications) are also considered.
Information level
Information extraction and enhancement: Through natural language processing (NLP) technology, more abundant information is extracted from the literature, such as the novelty of research topics, the innovation of research methods, the practicability of research results, etc.
Semantic analysis: analyze the semantic information of the research content, and identify the core contribution of the research and its influence on the existing knowledge system.
Knowledge level
Construction of knowledge map: build knowledge map in the field, connect new research results with existing knowledge system, and evaluate the innovation and complementarity of research.
Interdisciplinary evaluation: through the analysis of knowledge map, evaluate the contribution and influence of research in interdisciplinary field.
Wisdom level
Decision support system: Based on AI technology, it provides decision support for academic evaluation, such as automatically identifying high-quality research and recommending potential research partners.
Value and ethical considerations: ethical and social responsibility considerations are integrated into the evaluation process to evaluate the social value and moral impact of research.
Purpose level
Diversified evaluation objectives: establish diversified evaluation objectives, including promoting scientific innovation, supporting social progress and encouraging open science.
Dynamic adjustment and feedback: according to the changes of scientific research environment and social needs, dynamically adjust the evaluation mechanism and establish a feedback mechanism to promote the continuous optimization of evaluation standards.
The new evaluation mechanism based on DIKWP will no longer rely solely on traditional quantitative indicators, but comprehensively use artificial intelligence technology to conduct in-depth analysis and evaluation of academic achievements from the whole chain of data to wisdom. This mechanism can reflect the real value and social impact of research more accurately, and promote the quality improvement of scientific research and the healthy development of scientific research ecology.
6 Realization of the reform of traditional academic evaluation mechanism
Based on the DIKWP model and the transformation from conceptual space to semantic space, the reform of the traditional academic evaluation mechanism can be realized through the following detailed processes:
6.1 Initial stage: building the basic framework
Define new evaluation indicators: with the cooperation of multidisciplinary experts, define a comprehensive evaluation system including quantitative and qualitative indicators. These indicators not only cover the traditional influencing factors and citation times, but also include the innovation, social influence and interdisciplinary contribution of the research.
Construction of technical platform: develop a platform based on AI technology, and use natural language processing, data mining, semantic analysis and other technologies to automatically collect and analyze relevant data of academic achievements.
6.2 Development Stage: Integration and Optimization
Automatic processing at the data level: use AI technology to automatically collect the data of academic publications, such as downloads and social media mentions, and integrate these data to enhance the richness of information.
In-depth analysis of information level: through NLP technology, the text content is deeply analyzed, and the core concepts and contributions of the research are extracted, as well as its supplements and challenges to the existing knowledge system.
Connection and mapping at the knowledge level: construct a domain knowledge map, link new research results with the existing knowledge system, and evaluate their position and contribution in the knowledge system.
6.3 Mature stage: intelligent decision-making and social feedback
Intelligent decision support: Based on the evaluation system and integrated data, AI system provides decision support, such as identifying and recommending high-impact research and evaluating the social value of research.
Dynamic adjustment of purpose level: dynamically adjust the evaluation system and indicators according to the changes of social needs and scientific research environment to ensure the timeliness and adaptability of the evaluation mechanism.
Social impact feedback: through communication with academia, industry and the public, collect feedback on the new evaluation mechanism, and constantly optimize and adjust the evaluation indicators and processes.
6.4 Long-term Maintenance and Innovation
Continuous technological innovation: With the development of AI and data science and technology, new data analysis and processing technologies are continuously explored to improve the accuracy and efficiency of the evaluation mechanism.
Openness and transparency: maintain the openness and transparency of the evaluation mechanism, encourage academic communities and the public to participate in the evaluation process, and improve the credibility of the evaluation system.
Globalization cooperation: cooperate with international academic organizations and research institutions to promote the unification of evaluation standards on a global scale and promote international academic exchanges and cooperation.
Through this process, the new academic evaluation mechanism based on DIKWP model can not only evaluate the quality and influence of research more accurately, but also promote the healthy development of scientific research and the diversity of scientific research ecology. This change will be a gradual process, which requires the joint efforts and support of scientific research, technology and society.
7 How to connect, integrate and transition the traditional evaluation mechanism with the DIKWP evaluation mechanism?
In the current academic circles, the traditional evaluation mechanism mainly relies on peer review, impact factors, cited times and other indicators. Although these indicators provide some indications of the influence and quality of research, they also have many limitations, such as focusing too much on quantity and ignoring quality and the value of interdisciplinary research. With the development of artificial intelligence (AI) technology and the proposal of DIKWP (Data, Information, Knowledge, Wisdom and purpose) model, the possibility of applying this new model to academic evaluation appears, in order to build a more comprehensive, fair and dynamic evaluation system. The following is a detailed discussion on how to connect, integrate and transition the traditional evaluation mechanism with the evaluation mechanism based on DIKWP.
7.1 Convergence stage: integration of data and information
In the convergence stage, the first task is to integrate the data and information from the traditional evaluation mechanism into the DIKWP model. This includes transforming existing academic publications, cited data, impact factors, etc. into data (d) and information (i) input under the framework of DIKWP. Through AI technology, such as natural language processing (NLP) and data mining, we can automatically extract relevant data and information from a large number of academic documents and evaluate their quality and relevance. This process not only improves the efficiency of data processing, but also lays the foundation for further analysis.
7.2 Integration stage: deepening of knowledge and wisdom
The core of the fusion stage is to use the extracted data and information to construct a deep understanding of knowledge (K) through semantic analysis and knowledge mapping technology. At this stage, the research results are no longer regarded as independent data points, but as a part of the knowledge system, and its value and significance lie in its association with existing knowledge and its contribution to the knowledge system. In addition, the evaluation at the level of wisdom (W) has also begun to get attention, and the evaluation mechanism has begun to consider the social impact, ethical considerations and practical application value of the research, rather than just paying attention to its theoretical contribution.
7.3 Transition stage: introduction of purpose level and dynamic adjustment of evaluation system
The key to the transition stage lies in the introduction of the level of purpose (P), that is, considering the ultimate goal of research and the embodiment of purpose in the evaluation system. This requires that the evaluation mechanism can identify and evaluate the motives and objectives behind the research and its potential contribution to academia and society. At this time, traditional evaluation indicators (such as impact factors and cited times) and evaluation indicators based on DIKWP model need to be dynamically integrated to form a multi-evaluation system that can not only reflect the immediate impact of research, but also evaluate its long-term value and social impact.
7.4 Improvement stage: building a comprehensive evaluation platform based on DIKWP
In the improvement stage, the evaluation mechanism based on DIKWP will be fully formed and become a comprehensive evaluation platform, including not only traditional evaluation indicators, but also comprehensive evaluation based on data, information, knowledge, wisdom and purpose. This platform will be highly flexible and dynamic, and can adjust the evaluation criteria and indicators in time according to the changes in academia and society. It will adopt AI and machine learning technology, and constantly learn and adapt from new research results, so as to improve the accuracy and foresight of evaluation. In addition, the platform will encourage openness and transparency, allow researchers and reviewers to directly participate in the evaluation process, and increase the interactivity and participation of the evaluation mechanism.
7.5 Implementation stage: continuous optimization and community participation
Finally, in order to ensure the effectiveness and adaptability of the evaluation mechanism, it is necessary to establish a continuous optimization mechanism and extensive community participation. This involves regular evaluation system review, updating evaluation standards, introducing new evaluation techniques and methods, and collecting and responding to feedback from academia and society. Community participation is particularly important, because it ensures that the evaluation system can truly reflect the needs and values of academia and society, and at the same time promotes academic exchanges and cooperation.
The connection, integration and transition between the traditional evaluation mechanism and the evaluation mechanism based on DIKWP model marks the evolution of academic evaluation to a more comprehensive, dynamic and participatory direction. By integrating AI technology and DIKWP model, the new evaluation system can not only evaluate the quality and influence of research more accurately, but also consider the social value and long-term significance of research. This change will promote the healthy development of academic research, encourage interdisciplinary and innovative research, and provide researchers with a fairer, more transparent and diversified evaluation environment. With the progress of technology and the constant changes in academic circles, the evaluation mechanism based on DIKWP will continue to evolve and improve, making greater contributions to academic research and social progress. The reform of academic evaluation mechanism based on DIKWP model is an important supplement and development to the traditional academic evaluation system. By introducing artificial intelligence technology and DIKWP model, the new evaluation mechanism can evaluate the quality and influence of research more accurately and comprehensively, while considering the social value and long-term significance of research. This reform not only promotes the healthy development of academic research, but also encourages interdisciplinary and innovative research, and improves the fairness, transparency and diversity of evaluation. With the continuous progress of technology and the change of academic needs, the evaluation mechanism based on DIKWP will continue to evolve and make greater contributions to global academic research and social progress.
随着人工智能技术的快速发展，学术评价机制面临着前所未有的挑战与机遇。传统的评价机制，如SCI检索、影响因子和高被引论文等，虽然在学术界有着广泛的应用和认可，但在评价的全面性、动态性和参与性方面存在限制。本报告探讨了基于DIKWP模型（数据、信息、知识、智慧、意图）的新型学术评价机制，该机制致力于衔接和融合传统与现代评价方法，通过整合人工智能技术和DIKWP模型，提出了一个更加全面、公平和透明的学术评价体系。报告详细描述了新评价机制的设计原则、实施步骤和预期效果，以及如何通过社区参与和持续优化确保评价体系的适应性和有效性。通过这一变革，旨在促进学术研究的健康发展，鼓励创新性研究，同时为研究者提供一个更加多元和参与性的评价环境。
2023年以来在AI技术颠覆下的传统同行评议机制的情景中，我们可以预见一系列深刻的变革：
自动化的初步筛选过程：AI技术可以自动分析提交的学术论文，基于预设的质量标准和相关性标准进行初步筛选。这不仅大大减少了人工筛选的负担，还提高了筛选过程的效率和公正性。
深度内容分析：AI系统能够深入分析论文的内容，识别出新颖性、研究方法的准确性、数据分析的有效性等关键因素。通过深度学习和自然语言处理技术，AI能够理解复杂的学术论文，提供与人类专家相媲美乃至超越的评审意见。
预测论文影响力：利用大数据和机器学习算法，AI技术可以预测论文未来的引用量和学术影响力。这为编辑和评审提供了一个重要的参考依据，帮助他们识别那些可能会对学术界产生重大影响的研究工作。
检测学术不端行为：AI技术能够有效检测论文中的剽窃、数据伪造、重复发表等学术不端行为。通过对海量文献数据库的分析，AI可以迅速识别出论文中可能存在的问题，保障学术的纯洁性。
个性化的匹配专家：AI系统可以根据论文的内容和研究方向，自动匹配最合适的评审专家。这一过程考虑了专家的研究兴趣、专业背景和评审历史，确保每篇论文都能得到最公正和专业的评审。
实时反馈和交互系统：AI技术可以建立一个平台，允许作者和评审之间进行更直接和实时的交流。这种交互不仅加快了评审过程，还提高了评审的透明度和互动性。
持续学习和自我优化：随着时间的推移，AI系统通过不断学习新的数据和评审结果，其评审质量和准确性将持续提升。这种自我优化的能力使得AI技术成为一个不断进化的系统，能够适应学术界不断变化的需求。
在AI技术颠覆下的传统同行评议机制将变得更加高效、公正和智能。这种变革不仅提升了学术出版的质量和速度，还为研究人员提供了更加便捷和互动的学术交流环境，推动了整个学术界的进步和创新。
在未来几年里，我们可以预期AI技术在同行评议机制中的应用将经历从初步尝试到全面实施的过程。以下是这些变革可能发生的时间进程：
2024-2025年：初步尝试和验证
研究阶段：学术界和技术开发者开始合作，研究如何将AI技术有效地应用于同行评议过程中。期间将会有少量的试验性项目启动，用于验证AI在自动化初步筛选、内容分析以及检测学术不端行为等方面的有效性。
技术验证：这一阶段的成功案例将证明AI技术在提高评审效率和准确性方面的潜力，但同时也会暴露出技术实施的初期挑战，如评审标准的制定、AI系统的训练数据质量等。
2026-2027年：技术完善和初步应用
技术迭代：根据初期试验的反馈，技术开发者将不断完善AI系统，改进算法，扩大数据库，以适应更广泛的学术领域和复杂的评审需求。
初步应用：更多的学术期刊和会议开始采用AI技术进行论文的初步筛选和内容分析，AI辅助的同行评议成为越来越多编辑和学者接受的新常态。
2028-2029年：广泛应用和社区反馈
广泛应用：随着AI技术的成熟和社区对AI辅助评审机制信任度的提高，越来越多的学术出版物和组织开始全面采用AI技术进行同行评议。
社区反馈：学术社区开始对AI评审机制的效果进行广泛的讨论和反馈，进一步推动技术的优化和改进。同时，也会涌现出针对AI评审的伦理和道德讨论，促进评审机制的透明度和公平性。
2030年及以后：持续优化和深度整合
持续优化：基于不断收集的数据和反馈，AI系统将进入持续优化和学习的阶段，其评审质量和准确性逐渐超过或达到人类专家水平。
深度整合：AI技术将不仅仅在同行评议过程中发挥作用，还将深度整合到学术研究的各个方面，包括研究设计、数据分析、结果验证等，形成一个全面智能化的学术研究和出版生态。
AI技术在同行评议机制中的应用将是一个逐步发展的过程，从最初的尝试和验证到技术的完善、广泛应用，再到深度整合和持续优化。这一过程不仅需要技术的进步，还需要学术界、技术开发者以及社会各界的共同努力和适应。随着时间的推移，我们可以预见一个更加高效、公正和智能的同行评议新时代的到来。
随着人工智能技术在同行评议过程中的广泛应用，我们将见证对当前学术评价机制的深刻变革，尤其是对SCI检索、影响因子和高被引论文等重要指标的影响。以下是这些变化可能带来的具体影响和变革：
更广泛的包容性：AI技术能够更高效地识别和评价跨学科研究的价值，这可能导致SCI检索系统包含更多领域的研究，增加跨学科研究的可见性。
检索质量提升：AI辅助的检索系统能够更准确地理解研究内容和语境，提高检索结果的相关性和准确性，从而提升研究人员检索文献的效率。
评价标准的多元化：AI技术将使得影响因子的计算不再仅仅依赖于引用数量，还可能包括论文的社会影响、跨学科影响等多维度的评价标准。
实时动态更新：借助AI的实时数据处理能力，影响因子可能变成一个动态更新的指标，更真实地反映期刊或文章的即时影响力。
内容质量的重视：高被引论文的评价可能不再仅仅依赖于被引次数，AI技术能够深入分析论文的创新性、学术质量和社会价值，形成更全面的评价机制。
快速发现和推广：AI技术能够快速识别潜在的高质量研究，促进其在学术界的快速传播和应用，加速科学知识的迭代更新。
公平性和透明度的提升：AI技术的应用将减少人为偏见和主观性的影响，提高学术评价的公平性和透明度。
适应性和灵活性增强：随着学术研究的不断发展和变化，AI技术能够快速适应新的学术趋势和评价需求，使学术评价机制更加灵活和时效。
促进学术创新：AI辅助的评价机制将更加重视研究的原创性和创新性，鼓励学者探索新领域、新方法，从而推动学术创新和科学进步。
人工智能技术将对现有的学术评价机制产生深远的影响，推动其向更加公平、透明、多元和动态的方向发展。这些变革将有助于更好地激励和评价学术成就，促进学术研究的健康发展。
借助DIKWP模型进行深入分析，我们可以从数据、信息、知识、智慧、意图（DIKWP）的角度来理解人工智能技术对学术评价机制变革的影响：
数据（Data）
在变革过程中，数据作为基础层面，指的是关于期刊引用、论文质量、跨学科研究等的原始信息。人工智能技术能够有效地处理和分析这些大量复杂的数据，为学术评价提供更准确和全面的基础支撑。例如，通过深度学习算法，可以从文献中提取关键数据，如引用次数、研究领域、作者贡献等，为后续的信息提炼和知识形成打下基础。
信息（Information）
信息层面涉及将原始数据转化为有用的信息，如通过算法分析识别出高质量研究、潜在的跨学科影响等。AI技术在这一层面的应用，使得从大量数据中提炼出的信息更加精准、有针对性，为学术评价提供了更为丰富和深入的内容。
知识（Knowledge）
知识层面关注于从信息中提取和构建有价值的知识，如理解影响因子的深层含义、构建跨学科研究的评价标准等。AI技术通过对信息的深度分析和学习，有助于形成对学术评价新标准的理解和知识，从而推动评价机制的创新和优化。
智慧（Wisdom）
智慧层面强调应用知识来进行明智的决策和判断，如决定哪些新的评价标准应该被引入学术评价体系。AI技术在此层面的应用，能够基于深度知识和数据分析，为学术评价机制的改进提供策略性的建议，确保评价标准的公正性、全面性和前瞻性。
意图（Purpose）
最终，意图层面关注于通过改革学术评价机制来实现的长远目标，如促进学术创新、保障研究质量、激励学术交流等。通过DIKWP模型的应用，人工智能技术的引入不仅仅是改变评价机制的手段，更是为了实现更高层次的目标——创建一个更加健康、公正、激励创新的学术生态系统。
DIKWP模型为我们提供了一个全面分析人工智能技术如何影响学术评价机制变革的框架。从数据的收集和处理，到信息的提炼，再到知识的构建和智慧的应用，直至实现评价机制改革的最终意图，人工智能技术在每一个层面上都将发挥关键作用，推动学术评价向更加科学、合理、激励创新的方向发展。
基于DIKWP模型和从概念空间到语义空间的转换，设想一个新的学术评价机制，这个机制不仅关注传统的量化指标（如引用次数、影响因子等），而且深入挖掘学术成果的语义价值和社会影响，从而更全面地评价研究的质量和贡献。下面是基于DIKWP模型设计的新评价机制的畅想：
数据（Data）层面
自动化数据收集：使用AI技术自动收集关于学术出版物的原始数据，包括但不限于引用次数、下载量、社交媒体提及次数等。
多维度数据整合：除了传统指标外，还考虑研究的跨学科性、研究团队的多样性、研究的开放性（如开放获取出版物）等非传统数据指标。
信息（Information）层面
信息提炼与增强：通过自然语言处理（NLP）技术，从文献中提取更丰富的信息，如研究主题的新颖性、研究方法的创新性、研究结果的实用性等。
语义分析：分析研究内容的语义信息，识别研究的核心贡献和对现有知识体系的影响。
知识（Knowledge）层面
知识图谱构建：构建领域内的知识图谱，将新的研究成果与现有知识体系相连接，评估研究的创新性和补充性。
跨学科评价：通过知识图谱分析，评估研究在跨学科领域中的贡献和影响力。
智慧（Wisdom）层面
决策支持系统：基于AI技术，为学术评价提供决策支持，如自动识别高质量研究、推荐潜在的研究合作伙伴等。
价值与伦理考量：在评价过程中融入伦理和社会责任的考量，评价研究的社会价值和道德影响。
意图（Purpose）层面
多元化评价目标：确立多元化的评价目标，包括促进科学创新、支持社会进步、鼓励开放科学等。
动态调整与反馈：根据科研环境和社会需求的变化，动态调整评价机制，建立反馈机制促进评价标准的持续优化。
基于DIKWP的新评价机制将不再单一依赖传统的量化指标，而是综合利用人工智能技术，从数据到智慧的全链条对学术成果进行深入分析和评价。这种机制能够更准确地反映研究的真实价值和社会影响，促进科学研究的质量提升和科研生态的健康发展。
基于DIKWP模型和从概念空间到语义空间的转换，对传统学术评价机制的变革可以通过以下详细进程实现：
定义新的评价指标：在多学科专家的共同协作下，定义一套包含量化和质化指标的综合评价体系。这些指标不仅涵盖传统的影响因子和引用次数，还包括研究的创新性、社会影响、跨学科贡献等。
技术平台搭建：开发基于AI技术的平台，利用自然语言处理、数据挖掘、语义分析等技术，自动收集和分析学术成果的相关数据。
数据层面的自动化处理：利用AI技术自动化收集学术出版物的数据，如下载量、社交媒体提及次数等，并整合这些数据以增强信息的丰富度。
信息层面的深度解析：通过NLP技术深入解析文本内容，提炼出研究的核心概念和贡献，以及其对现有知识体系的补充和挑战。
知识层面的连接与映射：构建领域知识图谱，将新的研究成果与现有知识体系相链接，评估其在知识体系中的位置和贡献。
智慧层面的决策支持：基于评价体系和集成的数据，AI系统提供决策支持，如识别和推荐高影响研究、评估研究的社会价值等。
意图层面的动态调整：根据社会需求和科研环境的变化，动态调整评价体系和指标，确保评价机制的时效性和适应性。
社会影响反馈：通过与学术界、工业界和公众的交流，收集对新评价机制的反馈，不断优化和调整评价指标和流程。
持续的技术创新：随着AI和数据科学技术的发展，不断探索新的数据分析和处理技术，提高评价机制的准确性和效率。
开放性和透明性：保持评价机制的开放性和透明性，鼓励学术社区和公众参与评价过程，提高评价体系的公信力。
全球化合作：与国际学术组织和研究机构合作，推动全球范围内的评价标准统一，促进国际学术交流和合作。
通过这一进程，基于DIKWP模型的新学术评价机制不仅能够更准确地评价研究的质量和影响，还能促进科学研究的健康发展和科研生态的多样性。这个变革将是一个渐进的过程，需要科研界、技术界和社会各方的共同努力和支持。
在当前的学术界，传统评价机制主要依赖于同行评议、影响因子、被引次数等指标，这些指标虽然提供了研究影响力和质量的一定指示，但也存在诸多局限性，如过分侧重于数量而忽视质量、忽略跨学科研究的价值等。随着人工智能（AI）技术的发展和DIKWP（数据、信息、知识、智慧、意图）模型的提出，出现了将这一新模型应用于学术评价的可能性，以期构建更加全面、公正和动态的评价体系。下面详细论述传统评价机制如何与基于DIKWP的评价机制衔接、融合与过渡。
在衔接阶段，首要任务是将传统评价机制中的数据和信息整合到DIKWP模型中。这包括将现有的学术出版物、引用数据、影响因子等转化为DIKWP框架下的数据（D）和信息（I）层面的输入。通过AI技术，如自然语言处理（NLP）和数据挖掘，可以自动化地从大量学术文献中提取相关数据和信息，同时评估其质量和相关性。这一过程不仅提高了数据处理的效率，也为后续的深度分析奠定了基础。
融合阶段的核心是利用提取的数据和信息，通过语义分析和知识图谱技术，构建知识（K）层面的深度理解。在这一阶段，研究成果不再仅仅被视为独立的数据点，而是作为知识体系中的一部分，其价值和意义在于与现有知识的关联和对知识体系的贡献。此外，智慧（W）层面的评价也开始得到重视，评价机制开始考虑研究的社会影响、伦理考量和实际应用价值，而非仅仅关注其理论贡献。
过渡阶段的关键在于意图（P）层面的引入，即考虑研究的最终目标和意图在评价体系中的体现。这要求评价机制能够识别和评估研究背后的动机、目标以及其对学术界和社会的潜在贡献。此时，传统评价指标（如影响因子和被引次数）与基于DIKWP模型的评价指标需要被动态整合，形成一个既能反映研究即时影响力，又能评估其长远价值和社会影响的多元评价体系。
在完善阶段，基于DIKWP的评价机制将完全形成，成为一个综合性的评价平台，不仅包括传统评价指标，也融入了基于数据、信息、知识、智慧和意图的全面评价。这一平台将具备高度的灵活性和动态性，能够根据学术界和社会的变化及时调整评价标准和指标。它将采用AI和机器学习技术，不断从新的研究成果中学习和适应，以提高评价的准确性和前瞻性。此外，该平台将鼓励开放性和透明性，允许研究者和评审者直接参与到评价过程中，增加评价机制的互动性和参与度。
最后，为确保评价机制的有效性和适应性，需要建立一个持续优化的机制和广泛的社区参与。这涉及到定期的评价体系审查、更新评价标准、引入新的评价技术和方法，以及收集和响应学术界和社会的反馈。社区参与尤为重要，因为它确保了评价体系能够真正反映学术界和社会的需求和价值观，同时促进了学术交流和合作。
传统评价机制与基于DIKWP模型的评价机制的衔接、融合与过渡，标志着学术评价向更加全面、动态和参与性方向的演进。通过整合AI技术和DIKWP模型，新的评价体系不仅能够更准确地评估研究的质量和影响力，也能够考虑研究的社会价值和长远意义。这一转变将促进学术研究的健康发展，鼓励跨学科和创新性研究，同时也为研究者提供更公平、透明和多元的评价环境。随着技术的进步和学术界的不断变化，基于DIKWP的评价机制将不断演化和完善，为学术研究和社会进步做出更大的贡献。基于DIKWP模型的学术评价机制变革是对传统学术评价体系的重要补充和发展。通过引入人工智能技术和DIKWP模型，新的评价机制能够更准确、全面地评估研究的质量和影响力，同时考虑研究的社会价值和长远意义。这一变革不仅促进了学术研究的健康发展，还鼓励了跨学科和创新性研究，提高了评价的公平性、透明性和多元性。随着技术的不断进步和学术界需求的变化，基于DIKWP的评价机制将持续演化，为全球学术研究和社会进步做出更大贡献。
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Data can be regarded as a concrete manifestation of the same semantics in our cognition. Often, Data represents the semantic confirmation of the existence of a specific fact or observation, and is recognised as the same object or concept by corresponding to some of the same semantic correspondences contained in the existential nature of the cognitive subject's pre-existing cognitive objects. When dealing with data, we often seek and extract the particular identical semantics that labels that data, and then unify them as an identical concept based on the corresponding identical semantics. For example, when we see a flock of sheep, although each sheep may be slightly different in terms of size, colour, gender, etc., we will classify them into the concept of "sheep" because they share our semantic understanding of the concept of "sheep". The same semantics can be specific, for example, when identifying an arm, we can confirm that a silicone arm is an arm based on the same semantics as a human arm, such as the same number of fingers, the same colour, the same arm shape, etc., or we can determine that the silicone arm is not an arm because it doesn't have the same semantics as a real arm, which is defined by the definition of "can be rotated". It is also possible to determine that the silicone arm is not an arm because it does not have the same semantics as a real arm, such as "rotatable".
Information, on the other hand, corresponds to the expression of different semantics in cognition. Typically, Information refers to the creation of new semantic associations by linking cognitive DIKWP objects with data, information, knowledge, wisdom, or purposes already cognised by the cognising subject through a specific purpose. When processing information, we identify the differences in the DIKWP objects they are cognised with, corresponding to different semantics, and classify the information according to the input data, information, knowledge, wisdom or purpose. For example, in a car park, although all cars can be classified under the notion of 'car', each car's parking location, time of parking, wear and tear, owner, functionality, payment history and experience all represent different semantics in the information. The different semantics of the information are often present in the cognition of the cognitive subject and are often not explicitly expressed. For example, a depressed person may use the term "depressed" to express the decline of his current mood relative to his previous mood, but this "depressed" is not the same as the corresponding information because its contrasting state is not the same as the corresponding information. However, the corresponding information cannot be objectively perceived by the listener because the contrasting state is not known to the listener, and thus becomes the patient's own subjective cognitive information.
Knowledge corresponds to the complete semantics in cognition. Knowledge is the understanding and explanation of the world acquired through observation and learning. In processing knowledge, we abstract at least one concept or schema that corresponds to a complete semantics through observation and learning. For example, we learn that all swans are white through observation, which is a complete knowledge of the concept "all swans are white" that we have gathered through a large amount of information.
Wisdom corresponds to information in the perspective of ethics, social morality, human nature, etc., a kind of extreme values from the culture, human social groups relative to the current era fixed or individual cognitive values. When dealing with Wisdom, we integrate this data, information, knowledge, and wisdom and use them to guide decision-making. For example, when faced with a decision-making problem, we integrate various perspectives such as ethics, morality, and feasibility, not just technology or efficiency.
Purpose can be viewed as a dichotomy (input, output), where both input and output are elements of data, information, knowledge, wisdom, or purpose. Purpose represents our understanding of a phenomenon or problem (input) and the goal we wish to achieve by processing and solving that phenomenon or problem (output). When processing purposes, the AI system processes the inputs according to its predefined goals (outputs), and gradually brings the outputs closer to the predefined goals by learning and adapting.
Yucong Duan, male, currently serves as a member of the Academic Committee of the School of Computer Science and Technology at Hainan University. He is a professor and doctoral supervisor and is one of the first batch of talents selected into the South China Sea Masters Program of Hainan Province and the leading talents in Hainan Province. He graduated from the Software Research Institute of the Chinese Academy of Sciences in 2006, and has successively worked and visited Tsinghua University, Capital Medical University, POSCO University of Technology in South Korea, National Academy of Sciences of France, Charles University in Prague, Czech Republic, Milan Bicka University in Italy, Missouri State University in the United States, etc. He is currently a member of the Academic Committee of the School of Computer Science and Technology at Hainan University and he is the leader of the DIKWP (Data, Information, Knowledge, Wisdom, Purpose) Innovation Team at Hainan University, Distinguished Researcher at Chongqing Police College, Leader of Hainan Provincial Committee's "Double Hundred Talent" Team, Vice President of Hainan Invention Association, Vice President of Hainan Intellectual Property Association, Vice President of Hainan Low Carbon Economy Development Promotion Association, Vice President of Hainan Agricultural Products Processing Enterprises Association, Director of Network Security and Informatization Association of Hainan Province, Director of Artificial Intelligence Society of Hainan Province, Visiting Fellow, Central Michigan University, Member of the Doctoral Steering Committee of the University of Modena. Since being introduced to Hainan University as a D-class talent in 2012, He has published over 260 papers, included more than 120 SCI citations, and 11 ESI citations, with a citation count of over 4300. He has designed 241 serialized Chinese national and international invention patents (including 15 PCT invention patents) for multiple industries and fields and has been granted 85 Chinese national and international invention patents as the first inventor. Received the third prize for Wu Wenjun's artificial intelligence technology invention in 2020; In 2021, as the Chairman of the Program Committee, independently initiated the first International Conference on Data, Information, Knowledge and Wisdom - IEEE DIKW 2021; Served as the Chairman of the IEEE DIKW 2022 Conference Steering Committee in 2022; Served as the Chairman of the IEEE DIKW 2023 Conference in 2023. He was named the most beautiful technology worker in Hainan Province in 2022 (and was promoted nationwide); In 2022 and 2023, he was consecutively selected for the "Lifetime Scientific Influence Ranking" of the top 2% of global scientists released by Stanford University in the United States. Participated in the development of 2 international standards for IEEE financial knowledge graph and 4 industry knowledge graph standards. Initiated and co hosted the first International Congress on Artificial Consciousness (AC2023) in 2023.
Prof. Yucong Duan
DIKWP-AC Artificial Consciousness Laboratory
AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory
DIKWP research group, Hainan University
duanyucong@hotmail.com
https://blog.sciencenet.cn/blog-3429562-1421920.html
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